Real Time Speech Recognition in 1-Dimensional Using Convolution Neural Network

Abstract

Abstract: Automatic voice recognition is a hot topic in artificial intelligence and machine learning, with the goal of creating robots that can communicate with humans through speech. Speech is an information-dense communication that includes both linguistic and paralinguistic information. Emotion is a prime example of paralinguistic information that is partially communicated through speech. Developing machines that can grasp non-linguistic information like emotion simplifies humanmachine communication by making it more natural and straightforward. The effectiveness of convolutional neural networks in recognizing speech has been examined in this study.. The input characteristics of the networks were wide-band spectrograms of voice samples. The networks were trained using voice signals produced by actors while acting out a given mood. Our models were trained and evaluated using English-language speech datasets. Two degrees of augmentations were applied to the training data in each database. To regularize the networks, the dropout approach was used. Our findings revealed that the genderagnostic, language-agnostic CNN models attained state-of-the-art accuracy, beat previously published results in the literature, and matched or even surpassed human performance on benchmark databases. Future research should look at the deep learning models' capacity to recognize speech emotion using real-world speech signals.